Time Series Imputation with Multivariate Radial Basis Function Neural Network

TL;DR

This paper introduces a novel time series imputation method combining multivariate RBF neural networks and recurrent neural networks to effectively handle missing data, leveraging local and temporal information.

Contribution

It proposes the MIM-RBFNN model for local data estimation and extends it with MIRNN-CF to better utilize temporal information in missing value imputation.

Findings

MIM-RBFNN outperforms existing models on real-world datasets.

MIRNN-CF improves imputation accuracy by incorporating temporal dynamics.

The models effectively handle both random and non-random missing patterns.

Abstract

Researchers have been persistently working to address the issue of missing values in time series data. Numerous models have been proposed, striving to estimate the distribution of the data. The Radial Basis Functions Neural Network (RBFNN) has recently exhibited exceptional performance in estimating data distribution. In this paper, we propose a time series imputation model based on RBFNN. Our imputation model learns local information from timestamps to create a continuous function. Additionally, we incorporate time gaps to facilitate learning information considering the missing terms of missing values. We name this model the Missing Imputation Multivariate RBFNN (MIM-RBFNN). However, MIM-RBFNN relies on a local information-based learning approach, which presents difficulties in utilizing temporal information. Therefore, we propose an extension called the Missing Value Imputation Recurrent Neural Network with Continuous Function (MIRNN-CF) using the continuous function generated by MIM-RBFNN. We evaluate the performance using two real-world datasets with non-random missing and random missing patterns, and conduct an ablation study comparing MIM-RBFNN and MIRNN-CF.